Data Engineer

Context

Collective.work is building the next-generation AI-powered sourcing platform for recruiters. Our mission is to help talent teams identify, engage, and hire the best candidates faster through intelligent automation and data-driven insights. We operate at the intersection of data, AI, and recruiting workflows—where high-quality data infrastructure is critical to our success.

Missions

• Design and maintain scalable data pipelines (batch and real-time)
• Build and optimize ETL/ELT workflows across Azure and/or GCP
• Develop data models and architectures to support analytics and ML use cases
• Ensure data quality, integrity, and reliability across systems
• Collaborate with ML engineers to prepare and serve training datasets
• Monitor and improve pipeline performance, cost efficiency, and scalability
• Implement best practices for data governance, security, and compliance
• Contribute to tooling and infrastructure decisions

Tools & Environment

• Cloud: Azure (Data Factory, Synapse) and/or GCP (BigQuery, Dataflow)
• Data Processing: Python, SQL, Spark
• Orchestration: Airflow / Prefect
• Storage: Data lakes, warehouses
• Streaming: Kafka / PubSub (nice to have)
• DevOps: Docker, CI/CD

Working Conditions

• Flexible remote work environment
• Opportunity to work on a product at the cutting edge of AI and recruiting
• High ownership and impact from day one
• Collaborative, product-driven engineering culture
• Opportunity to shape the data foundation of a growing platform

1. 3+ years of experience in data engineering or similar role
2. Strong experience with Azure and/or GCP data ecosystems
3. Proficiency in Python and SQL
4. Experience building scalable ETL pipelines
5. Familiarity with data warehousing concepts and modeling
6. Understanding of distributed systems and big data tools (e.g., Spark)
7. Experience working with APIs and integrating external data sources
8. Strong problem-solving skills and attention to detail